Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fb23d7920b8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fb23d6c38d0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    
    input_real = tf.placeholder(tf.float32,(None,image_width,image_height,image_channels),name = 'real')
    input_z = tf.placeholder(tf.float32,(None,z_dim),name = 'fake')
    lr = tf.placeholder(tf.float32,name = 'learning_rate')

    return (input_real, input_z, lr)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator',reuse = reuse):
        alpha = 0.02
        
        x1 = tf.layers.conv2d(images,64,5,strides = 2,padding = 'same')
        relu1 = tf.maximum(alpha * x1,x1)
        
        x2 = tf.layers.conv2d(relu1,128,5,strides = 2,padding = 'same')
        bn2 = tf.layers.batch_normalization(x2,training = True)
        relu2 = tf.maximum(alpha * bn2,bn2)
        
        x3 = tf.layers.conv2d(relu2,256,5,strides = 2,padding = 'same')
        bn3 = tf.layers.batch_normalization(x3,training = True)
        relu3 = tf.maximum(alpha * bn3,bn3)
        
        flat = tf.reshape(relu3,(-1,4*4*256))
        logits = tf.layers.dense(flat,1)
        out = tf.sigmoid(logits)

    return (out,logits)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse=not is_train):
        
        alpha = 0.02
        # First fully connected layer
        x1 = tf.layers.dense(z, 2*2*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 2, 2, 512))
        x1 = tf.layers.batch_normalization(x1, training=True)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=True)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=True)
        x3 = tf.maximum(alpha * x3, x3)
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        
        out = tf.tanh(logits)
        
        return out
    
    
    
      


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss
    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)

    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])

    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps +=1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [14]:
batch_size = 32
z_dim = 100
learning_rate = 0.002
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 0.1433... Generator Loss: 4.0648
Epoch 1/2... Batch 20... Discriminator Loss: 0.1277... Generator Loss: 7.2319
Epoch 1/2... Batch 30... Discriminator Loss: 3.4697... Generator Loss: 0.1176
Epoch 1/2... Batch 40... Discriminator Loss: 0.2212... Generator Loss: 2.4641
Epoch 1/2... Batch 50... Discriminator Loss: 1.2169... Generator Loss: 1.0714
Epoch 1/2... Batch 60... Discriminator Loss: 0.6673... Generator Loss: 1.1544
Epoch 1/2... Batch 70... Discriminator Loss: 1.7493... Generator Loss: 4.3069
Epoch 1/2... Batch 80... Discriminator Loss: 0.3945... Generator Loss: 1.5910
Epoch 1/2... Batch 90... Discriminator Loss: 2.3720... Generator Loss: 0.1068
Epoch 1/2... Batch 100... Discriminator Loss: 1.3815... Generator Loss: 5.0632
Epoch 1/2... Batch 110... Discriminator Loss: 1.4745... Generator Loss: 0.3199
Epoch 1/2... Batch 120... Discriminator Loss: 3.8903... Generator Loss: 9.8832
Epoch 1/2... Batch 130... Discriminator Loss: 0.4781... Generator Loss: 1.9338
Epoch 1/2... Batch 140... Discriminator Loss: 0.0615... Generator Loss: 6.0650
Epoch 1/2... Batch 150... Discriminator Loss: 0.2941... Generator Loss: 1.7727
Epoch 1/2... Batch 160... Discriminator Loss: 0.7073... Generator Loss: 5.7549
Epoch 1/2... Batch 170... Discriminator Loss: 1.2639... Generator Loss: 0.6639
Epoch 1/2... Batch 180... Discriminator Loss: 0.9358... Generator Loss: 0.7973
Epoch 1/2... Batch 190... Discriminator Loss: 0.3228... Generator Loss: 2.1874
Epoch 1/2... Batch 200... Discriminator Loss: 1.8307... Generator Loss: 0.2332
Epoch 1/2... Batch 210... Discriminator Loss: 0.3846... Generator Loss: 1.6523
Epoch 1/2... Batch 220... Discriminator Loss: 0.5662... Generator Loss: 1.4093
Epoch 1/2... Batch 230... Discriminator Loss: 0.2793... Generator Loss: 2.6779
Epoch 1/2... Batch 240... Discriminator Loss: 0.7848... Generator Loss: 3.3913
Epoch 1/2... Batch 250... Discriminator Loss: 1.2813... Generator Loss: 0.7140
Epoch 1/2... Batch 260... Discriminator Loss: 1.3894... Generator Loss: 0.5618
Epoch 1/2... Batch 270... Discriminator Loss: 0.6276... Generator Loss: 1.7902
Epoch 1/2... Batch 280... Discriminator Loss: 1.9760... Generator Loss: 0.3153
Epoch 1/2... Batch 290... Discriminator Loss: 0.3954... Generator Loss: 2.3701
Epoch 1/2... Batch 300... Discriminator Loss: 1.4490... Generator Loss: 0.4172
Epoch 1/2... Batch 310... Discriminator Loss: 0.6689... Generator Loss: 0.9985
Epoch 1/2... Batch 320... Discriminator Loss: 1.3089... Generator Loss: 3.9136
Epoch 1/2... Batch 330... Discriminator Loss: 1.3161... Generator Loss: 0.4365
Epoch 1/2... Batch 340... Discriminator Loss: 1.0872... Generator Loss: 1.3519
Epoch 1/2... Batch 350... Discriminator Loss: 1.0359... Generator Loss: 2.1885
Epoch 1/2... Batch 360... Discriminator Loss: 1.2318... Generator Loss: 1.4658
Epoch 1/2... Batch 370... Discriminator Loss: 1.2926... Generator Loss: 0.4981
Epoch 1/2... Batch 380... Discriminator Loss: 0.9564... Generator Loss: 3.3445
Epoch 1/2... Batch 390... Discriminator Loss: 0.7832... Generator Loss: 1.0331
Epoch 1/2... Batch 400... Discriminator Loss: 1.5672... Generator Loss: 0.3900
Epoch 1/2... Batch 410... Discriminator Loss: 0.8273... Generator Loss: 1.0537
Epoch 1/2... Batch 420... Discriminator Loss: 1.0747... Generator Loss: 3.2298
Epoch 1/2... Batch 430... Discriminator Loss: 0.8062... Generator Loss: 1.6375
Epoch 1/2... Batch 440... Discriminator Loss: 0.7318... Generator Loss: 2.1565
Epoch 1/2... Batch 450... Discriminator Loss: 1.3218... Generator Loss: 0.4469
Epoch 1/2... Batch 460... Discriminator Loss: 1.2434... Generator Loss: 0.4702
Epoch 1/2... Batch 470... Discriminator Loss: 1.1930... Generator Loss: 3.1872
Epoch 1/2... Batch 480... Discriminator Loss: 1.9827... Generator Loss: 0.1764
Epoch 1/2... Batch 490... Discriminator Loss: 0.8167... Generator Loss: 0.8925
Epoch 1/2... Batch 500... Discriminator Loss: 1.5003... Generator Loss: 0.3717
Epoch 1/2... Batch 510... Discriminator Loss: 1.8475... Generator Loss: 3.6453
Epoch 1/2... Batch 520... Discriminator Loss: 0.8375... Generator Loss: 1.1366
Epoch 1/2... Batch 530... Discriminator Loss: 1.6362... Generator Loss: 3.1194
Epoch 1/2... Batch 540... Discriminator Loss: 1.4721... Generator Loss: 0.3803
Epoch 1/2... Batch 550... Discriminator Loss: 1.8375... Generator Loss: 0.2368
Epoch 1/2... Batch 560... Discriminator Loss: 0.8802... Generator Loss: 1.4426
Epoch 1/2... Batch 570... Discriminator Loss: 0.9382... Generator Loss: 1.0612
Epoch 1/2... Batch 580... Discriminator Loss: 1.1298... Generator Loss: 0.5612
Epoch 1/2... Batch 590... Discriminator Loss: 0.8996... Generator Loss: 0.9020
Epoch 1/2... Batch 600... Discriminator Loss: 0.8233... Generator Loss: 1.1569
Epoch 1/2... Batch 610... Discriminator Loss: 2.0032... Generator Loss: 0.1974
Epoch 1/2... Batch 620... Discriminator Loss: 0.9322... Generator Loss: 1.2676
Epoch 1/2... Batch 630... Discriminator Loss: 0.8362... Generator Loss: 1.1559
Epoch 1/2... Batch 640... Discriminator Loss: 1.1965... Generator Loss: 0.5413
Epoch 1/2... Batch 650... Discriminator Loss: 1.5219... Generator Loss: 0.4246
Epoch 1/2... Batch 660... Discriminator Loss: 1.5216... Generator Loss: 0.3949
Epoch 1/2... Batch 670... Discriminator Loss: 1.3699... Generator Loss: 0.4679
Epoch 1/2... Batch 680... Discriminator Loss: 1.1078... Generator Loss: 0.6090
Epoch 1/2... Batch 690... Discriminator Loss: 1.3751... Generator Loss: 0.4803
Epoch 1/2... Batch 700... Discriminator Loss: 1.1628... Generator Loss: 2.0070
Epoch 1/2... Batch 710... Discriminator Loss: 1.6503... Generator Loss: 0.3469
Epoch 1/2... Batch 720... Discriminator Loss: 1.1647... Generator Loss: 0.8640
Epoch 1/2... Batch 730... Discriminator Loss: 1.0410... Generator Loss: 1.7293
Epoch 1/2... Batch 740... Discriminator Loss: 1.1047... Generator Loss: 0.5882
Epoch 1/2... Batch 750... Discriminator Loss: 1.5812... Generator Loss: 0.3273
Epoch 1/2... Batch 760... Discriminator Loss: 0.8843... Generator Loss: 1.1900
Epoch 1/2... Batch 770... Discriminator Loss: 1.5085... Generator Loss: 0.3802
Epoch 1/2... Batch 780... Discriminator Loss: 1.4942... Generator Loss: 0.3633
Epoch 1/2... Batch 790... Discriminator Loss: 0.9663... Generator Loss: 0.7086
Epoch 1/2... Batch 800... Discriminator Loss: 0.8415... Generator Loss: 0.9773
Epoch 1/2... Batch 810... Discriminator Loss: 1.5293... Generator Loss: 0.3442
Epoch 1/2... Batch 820... Discriminator Loss: 0.8576... Generator Loss: 1.1152
Epoch 1/2... Batch 830... Discriminator Loss: 1.0307... Generator Loss: 1.8259
Epoch 1/2... Batch 840... Discriminator Loss: 2.0226... Generator Loss: 0.2043
Epoch 1/2... Batch 850... Discriminator Loss: 1.3506... Generator Loss: 0.4623
Epoch 1/2... Batch 860... Discriminator Loss: 1.1714... Generator Loss: 1.4639
Epoch 1/2... Batch 870... Discriminator Loss: 0.9608... Generator Loss: 0.9817
Epoch 1/2... Batch 880... Discriminator Loss: 0.7686... Generator Loss: 1.0978
Epoch 1/2... Batch 890... Discriminator Loss: 1.3530... Generator Loss: 0.4533
Epoch 1/2... Batch 900... Discriminator Loss: 1.2408... Generator Loss: 2.0752
Epoch 1/2... Batch 910... Discriminator Loss: 1.3432... Generator Loss: 0.5083
Epoch 1/2... Batch 920... Discriminator Loss: 1.1197... Generator Loss: 0.6896
Epoch 1/2... Batch 930... Discriminator Loss: 0.9195... Generator Loss: 0.9032
Epoch 1/2... Batch 940... Discriminator Loss: 0.9524... Generator Loss: 1.5864
Epoch 1/2... Batch 950... Discriminator Loss: 0.9796... Generator Loss: 1.4183
Epoch 1/2... Batch 960... Discriminator Loss: 0.7845... Generator Loss: 1.2634
Epoch 1/2... Batch 970... Discriminator Loss: 0.9963... Generator Loss: 0.7629
Epoch 1/2... Batch 980... Discriminator Loss: 1.1217... Generator Loss: 0.5749
Epoch 1/2... Batch 990... Discriminator Loss: 1.1963... Generator Loss: 0.5384
Epoch 1/2... Batch 1000... Discriminator Loss: 1.2363... Generator Loss: 0.4710
Epoch 1/2... Batch 1010... Discriminator Loss: 1.0763... Generator Loss: 0.5916
Epoch 1/2... Batch 1020... Discriminator Loss: 1.0534... Generator Loss: 0.9208
Epoch 1/2... Batch 1030... Discriminator Loss: 1.0133... Generator Loss: 0.9690
Epoch 1/2... Batch 1040... Discriminator Loss: 1.3199... Generator Loss: 0.4929
Epoch 1/2... Batch 1050... Discriminator Loss: 2.3860... Generator Loss: 0.1372
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Epoch 2/2... Batch 1820... Discriminator Loss: 1.0231... Generator Loss: 1.0008
Epoch 2/2... Batch 1830... Discriminator Loss: 1.2495... Generator Loss: 0.4479
Epoch 2/2... Batch 1840... Discriminator Loss: 1.0553... Generator Loss: 0.6613
Epoch 2/2... Batch 1850... Discriminator Loss: 0.6939... Generator Loss: 1.0974
Epoch 2/2... Batch 1860... Discriminator Loss: 1.8617... Generator Loss: 0.2433
Epoch 2/2... Batch 1870... Discriminator Loss: 0.7825... Generator Loss: 0.8500

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [15]:
batch_size = 32
z_dim = 100
learning_rate = 0.0008
beta1 = 0.3

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 0.1809... Generator Loss: 16.5037
Epoch 1/1... Batch 20... Discriminator Loss: 0.0948... Generator Loss: 6.1842
Epoch 1/1... Batch 30... Discriminator Loss: 3.0025... Generator Loss: 12.7487
Epoch 1/1... Batch 40... Discriminator Loss: 0.4698... Generator Loss: 4.2087
Epoch 1/1... Batch 50... Discriminator Loss: 1.2391... Generator Loss: 3.0904
Epoch 1/1... Batch 60... Discriminator Loss: 1.2628... Generator Loss: 0.5072
Epoch 1/1... Batch 70... Discriminator Loss: 0.7330... Generator Loss: 1.4166
Epoch 1/1... Batch 80... Discriminator Loss: 0.8326... Generator Loss: 4.3241
Epoch 1/1... Batch 90... Discriminator Loss: 0.6660... Generator Loss: 6.5351
Epoch 1/1... Batch 100... Discriminator Loss: 1.1415... Generator Loss: 0.5830
Epoch 1/1... Batch 110... Discriminator Loss: 0.9414... Generator Loss: 1.4170
Epoch 1/1... Batch 120... Discriminator Loss: 2.2746... Generator Loss: 3.2972
Epoch 1/1... Batch 130... Discriminator Loss: 1.0533... Generator Loss: 0.5463
Epoch 1/1... Batch 140... Discriminator Loss: 1.4249... Generator Loss: 0.4357
Epoch 1/1... Batch 150... Discriminator Loss: 0.4887... Generator Loss: 3.8288
Epoch 1/1... Batch 160... Discriminator Loss: 1.2521... Generator Loss: 2.5758
Epoch 1/1... Batch 170... Discriminator Loss: 0.9155... Generator Loss: 1.0174
Epoch 1/1... Batch 180... Discriminator Loss: 1.0828... Generator Loss: 3.3758
Epoch 1/1... Batch 190... Discriminator Loss: 0.4630... Generator Loss: 4.7566
Epoch 1/1... Batch 200... Discriminator Loss: 0.8027... Generator Loss: 2.9488
Epoch 1/1... Batch 210... Discriminator Loss: 1.8113... Generator Loss: 3.4201
Epoch 1/1... Batch 220... Discriminator Loss: 1.1581... Generator Loss: 0.5434
Epoch 1/1... Batch 230... Discriminator Loss: 1.2901... Generator Loss: 0.4781
Epoch 1/1... Batch 240... Discriminator Loss: 0.4823... Generator Loss: 1.6149
Epoch 1/1... Batch 250... Discriminator Loss: 1.2073... Generator Loss: 0.5205
Epoch 1/1... Batch 260... Discriminator Loss: 1.2733... Generator Loss: 3.8084
Epoch 1/1... Batch 270... Discriminator Loss: 1.0262... Generator Loss: 1.3004
Epoch 1/1... Batch 280... Discriminator Loss: 1.7689... Generator Loss: 0.2758
Epoch 1/1... Batch 290... Discriminator Loss: 1.8778... Generator Loss: 0.2804
Epoch 1/1... Batch 300... Discriminator Loss: 1.5346... Generator Loss: 0.5339
Epoch 1/1... Batch 310... Discriminator Loss: 0.8809... Generator Loss: 0.9662
Epoch 1/1... Batch 320... Discriminator Loss: 1.5075... Generator Loss: 0.3592
Epoch 1/1... Batch 330... Discriminator Loss: 0.9523... Generator Loss: 0.8001
Epoch 1/1... Batch 340... Discriminator Loss: 1.9068... Generator Loss: 4.0056
Epoch 1/1... Batch 350... Discriminator Loss: 1.5832... Generator Loss: 0.3221
Epoch 1/1... Batch 360... Discriminator Loss: 1.6010... Generator Loss: 0.3425
Epoch 1/1... Batch 370... Discriminator Loss: 0.4553... Generator Loss: 1.6426
Epoch 1/1... Batch 380... Discriminator Loss: 0.8380... Generator Loss: 1.5563
Epoch 1/1... Batch 390... Discriminator Loss: 1.1959... Generator Loss: 0.8526
Epoch 1/1... Batch 400... Discriminator Loss: 0.8026... Generator Loss: 0.9196
Epoch 1/1... Batch 410... Discriminator Loss: 0.6438... Generator Loss: 1.4975
Epoch 1/1... Batch 420... Discriminator Loss: 3.0617... Generator Loss: 3.2895
Epoch 1/1... Batch 430... Discriminator Loss: 0.6934... Generator Loss: 1.9441
Epoch 1/1... Batch 440... Discriminator Loss: 1.1397... Generator Loss: 2.0475
Epoch 1/1... Batch 450... Discriminator Loss: 0.8080... Generator Loss: 1.4130
Epoch 1/1... Batch 460... Discriminator Loss: 0.9427... Generator Loss: 0.8917
Epoch 1/1... Batch 470... Discriminator Loss: 2.1383... Generator Loss: 3.0164
Epoch 1/1... Batch 480... Discriminator Loss: 1.2033... Generator Loss: 0.6921
Epoch 1/1... Batch 490... Discriminator Loss: 0.9880... Generator Loss: 1.0027
Epoch 1/1... Batch 500... Discriminator Loss: 1.6034... Generator Loss: 0.3994
Epoch 1/1... Batch 510... Discriminator Loss: 1.2433... Generator Loss: 0.5629
Epoch 1/1... Batch 520... Discriminator Loss: 1.0978... Generator Loss: 1.5815
Epoch 1/1... Batch 530... Discriminator Loss: 1.0721... Generator Loss: 1.8418
Epoch 1/1... Batch 540... Discriminator Loss: 1.3722... Generator Loss: 1.4588
Epoch 1/1... Batch 550... Discriminator Loss: 0.8808... Generator Loss: 0.7888
Epoch 1/1... Batch 560... Discriminator Loss: 0.9858... Generator Loss: 0.7444
Epoch 1/1... Batch 570... Discriminator Loss: 0.6644... Generator Loss: 0.9227
Epoch 1/1... Batch 580... Discriminator Loss: 1.2394... Generator Loss: 1.9173
Epoch 1/1... Batch 590... Discriminator Loss: 1.3765... Generator Loss: 0.4533
Epoch 1/1... Batch 600... Discriminator Loss: 1.0963... Generator Loss: 2.6171
Epoch 1/1... Batch 610... Discriminator Loss: 0.9102... Generator Loss: 1.5771
Epoch 1/1... Batch 620... Discriminator Loss: 1.0482... Generator Loss: 1.6110
Epoch 1/1... Batch 630... Discriminator Loss: 1.5850... Generator Loss: 0.3336
Epoch 1/1... Batch 640... Discriminator Loss: 2.1530... Generator Loss: 0.1757
Epoch 1/1... Batch 650... Discriminator Loss: 3.7625... Generator Loss: 5.4055
Epoch 1/1... Batch 660... Discriminator Loss: 1.2060... Generator Loss: 0.6319
Epoch 1/1... Batch 670... Discriminator Loss: 0.8151... Generator Loss: 0.8471
Epoch 1/1... Batch 680... Discriminator Loss: 0.9008... Generator Loss: 0.7242
Epoch 1/1... Batch 690... Discriminator Loss: 1.5827... Generator Loss: 0.3370
Epoch 1/1... Batch 700... Discriminator Loss: 0.4804... Generator Loss: 1.5211
Epoch 1/1... Batch 710... Discriminator Loss: 1.3382... Generator Loss: 2.4778
Epoch 1/1... Batch 720... Discriminator Loss: 1.6350... Generator Loss: 2.3577
Epoch 1/1... Batch 730... Discriminator Loss: 0.8953... Generator Loss: 0.8349
Epoch 1/1... Batch 740... Discriminator Loss: 0.6534... Generator Loss: 0.9608
Epoch 1/1... Batch 750... Discriminator Loss: 1.6404... Generator Loss: 0.3561
Epoch 1/1... Batch 760... Discriminator Loss: 0.7208... Generator Loss: 1.7093
Epoch 1/1... Batch 770... Discriminator Loss: 0.8333... Generator Loss: 1.8873
Epoch 1/1... Batch 780... Discriminator Loss: 1.1778... Generator Loss: 1.9091
Epoch 1/1... Batch 790... Discriminator Loss: 2.0549... Generator Loss: 2.8360
Epoch 1/1... Batch 800... Discriminator Loss: 1.0266... Generator Loss: 1.5812
Epoch 1/1... Batch 810... Discriminator Loss: 0.8476... Generator Loss: 0.7315
Epoch 1/1... Batch 820... Discriminator Loss: 0.9770... Generator Loss: 0.6575
Epoch 1/1... Batch 830... Discriminator Loss: 0.8672... Generator Loss: 1.4864
Epoch 1/1... Batch 840... Discriminator Loss: 0.7082... Generator Loss: 0.8947
Epoch 1/1... Batch 850... Discriminator Loss: 1.3507... Generator Loss: 0.4179
Epoch 1/1... Batch 860... Discriminator Loss: 0.7916... Generator Loss: 1.0244
Epoch 1/1... Batch 870... Discriminator Loss: 1.0619... Generator Loss: 0.7627
Epoch 1/1... Batch 880... Discriminator Loss: 1.1498... Generator Loss: 0.5288
Epoch 1/1... Batch 890... Discriminator Loss: 0.8789... Generator Loss: 1.3739
Epoch 1/1... Batch 900... Discriminator Loss: 1.2778... Generator Loss: 0.5686
Epoch 1/1... Batch 910... Discriminator Loss: 1.3979... Generator Loss: 0.5884
Epoch 1/1... Batch 920... Discriminator Loss: 0.8525... Generator Loss: 0.9030
Epoch 1/1... Batch 930... Discriminator Loss: 1.0828... Generator Loss: 1.4849
Epoch 1/1... Batch 940... Discriminator Loss: 0.5717... Generator Loss: 1.0707
Epoch 1/1... Batch 950... Discriminator Loss: 1.2890... Generator Loss: 0.4293
Epoch 1/1... Batch 960... Discriminator Loss: 1.0737... Generator Loss: 1.6322
Epoch 1/1... Batch 970... Discriminator Loss: 1.0321... Generator Loss: 0.8105
Epoch 1/1... Batch 980... Discriminator Loss: 1.1735... Generator Loss: 1.5163
Epoch 1/1... Batch 990... Discriminator Loss: 0.8749... Generator Loss: 1.1638
Epoch 1/1... Batch 1000... Discriminator Loss: 2.3868... Generator Loss: 3.3305
Epoch 1/1... Batch 1010... Discriminator Loss: 1.4526... Generator Loss: 0.4045
Epoch 1/1... Batch 1020... Discriminator Loss: 0.9429... Generator Loss: 0.9262
Epoch 1/1... Batch 1030... Discriminator Loss: 0.6437... Generator Loss: 1.7995
Epoch 1/1... Batch 1040... Discriminator Loss: 1.0748... Generator Loss: 1.6278
Epoch 1/1... Batch 1050... Discriminator Loss: 1.2148... Generator Loss: 0.4784
Epoch 1/1... Batch 1060... Discriminator Loss: 0.4461... Generator Loss: 5.0961
Epoch 1/1... Batch 1070... Discriminator Loss: 1.4490... Generator Loss: 1.5068
Epoch 1/1... Batch 1080... Discriminator Loss: 1.3235... Generator Loss: 1.0831
Epoch 1/1... Batch 1090... Discriminator Loss: 0.8274... Generator Loss: 1.0761
Epoch 1/1... Batch 1100... Discriminator Loss: 1.2431... Generator Loss: 0.4259
Epoch 1/1... Batch 1110... Discriminator Loss: 1.9524... Generator Loss: 3.8874
Epoch 1/1... Batch 1120... Discriminator Loss: 0.5085... Generator Loss: 1.9037
Epoch 1/1... Batch 1130... Discriminator Loss: 1.4351... Generator Loss: 0.5679
Epoch 1/1... Batch 1140... Discriminator Loss: 2.0231... Generator Loss: 0.1948
Epoch 1/1... Batch 1150... Discriminator Loss: 0.7726... Generator Loss: 0.8520
Epoch 1/1... Batch 1160... Discriminator Loss: 0.7163... Generator Loss: 0.8579
Epoch 1/1... Batch 1170... Discriminator Loss: 1.7178... Generator Loss: 0.2892
Epoch 1/1... Batch 1180... Discriminator Loss: 1.1714... Generator Loss: 0.5074
Epoch 1/1... Batch 1190... Discriminator Loss: 0.5280... Generator Loss: 1.2455
Epoch 1/1... Batch 1200... Discriminator Loss: 1.3792... Generator Loss: 0.3886
Epoch 1/1... Batch 1210... Discriminator Loss: 1.4314... Generator Loss: 1.0876
Epoch 1/1... Batch 1220... Discriminator Loss: 1.1110... Generator Loss: 2.5233
Epoch 1/1... Batch 1230... Discriminator Loss: 1.6625... Generator Loss: 0.3184
Epoch 1/1... Batch 1240... Discriminator Loss: 1.2617... Generator Loss: 1.5325
Epoch 1/1... Batch 1250... Discriminator Loss: 0.8911... Generator Loss: 0.8458
Epoch 1/1... Batch 1260... Discriminator Loss: 1.1587... Generator Loss: 0.5559
Epoch 1/1... Batch 1270... Discriminator Loss: 0.9397... Generator Loss: 0.7029
Epoch 1/1... Batch 1280... Discriminator Loss: 1.0825... Generator Loss: 0.8012
Epoch 1/1... Batch 1290... Discriminator Loss: 1.3204... Generator Loss: 0.3756
Epoch 1/1... Batch 1300... Discriminator Loss: 1.6920... Generator Loss: 0.2630
Epoch 1/1... Batch 1310... Discriminator Loss: 0.6720... Generator Loss: 1.3968
Epoch 1/1... Batch 1320... Discriminator Loss: 2.9820... Generator Loss: 3.5772
Epoch 1/1... Batch 1330... Discriminator Loss: 1.0835... Generator Loss: 0.6324
Epoch 1/1... Batch 1340... Discriminator Loss: 2.1048... Generator Loss: 0.1672
Epoch 1/1... Batch 1350... Discriminator Loss: 1.3618... Generator Loss: 0.3935
Epoch 1/1... Batch 1360... Discriminator Loss: 0.9738... Generator Loss: 1.5218
Epoch 1/1... Batch 1370... Discriminator Loss: 1.5417... Generator Loss: 0.3748
Epoch 1/1... Batch 1380... Discriminator Loss: 0.8699... Generator Loss: 2.3322
Epoch 1/1... Batch 1390... Discriminator Loss: 0.6472... Generator Loss: 1.6600
Epoch 1/1... Batch 1400... Discriminator Loss: 1.5922... Generator Loss: 0.3299
Epoch 1/1... Batch 1410... Discriminator Loss: 1.3221... Generator Loss: 2.3058
Epoch 1/1... Batch 1420... Discriminator Loss: 1.0935... Generator Loss: 0.8322
Epoch 1/1... Batch 1430... Discriminator Loss: 1.2466... Generator Loss: 2.0969
Epoch 1/1... Batch 1440... Discriminator Loss: 0.8908... Generator Loss: 0.7578
Epoch 1/1... Batch 1450... Discriminator Loss: 0.1579... Generator Loss: 2.3792
Epoch 1/1... Batch 1460... Discriminator Loss: 0.9949... Generator Loss: 0.8596
Epoch 1/1... Batch 1470... Discriminator Loss: 0.9342... Generator Loss: 0.6313
Epoch 1/1... Batch 1480... Discriminator Loss: 1.4513... Generator Loss: 0.3211
Epoch 1/1... Batch 1490... Discriminator Loss: 1.0806... Generator Loss: 0.6508
Epoch 1/1... Batch 1500... Discriminator Loss: 2.8237... Generator Loss: 3.8605
Epoch 1/1... Batch 1510... Discriminator Loss: 0.7698... Generator Loss: 1.0413
Epoch 1/1... Batch 1520... Discriminator Loss: 0.9263... Generator Loss: 1.2287
Epoch 1/1... Batch 1530... Discriminator Loss: 1.3227... Generator Loss: 1.1902
Epoch 1/1... Batch 1540... Discriminator Loss: 1.0924... Generator Loss: 0.6810
Epoch 1/1... Batch 1550... Discriminator Loss: 1.1920... Generator Loss: 0.5410
Epoch 1/1... Batch 1560... Discriminator Loss: 1.4209... Generator Loss: 2.7915
Epoch 1/1... Batch 1570... Discriminator Loss: 1.0580... Generator Loss: 0.5394
Epoch 1/1... Batch 1580... Discriminator Loss: 1.0318... Generator Loss: 0.6954
Epoch 1/1... Batch 1590... Discriminator Loss: 2.5442... Generator Loss: 0.1080
Epoch 1/1... Batch 1600... Discriminator Loss: 1.3657... Generator Loss: 2.4170
Epoch 1/1... Batch 1610... Discriminator Loss: 1.0510... Generator Loss: 0.5438
Epoch 1/1... Batch 1620... Discriminator Loss: 0.8812... Generator Loss: 0.6277
Epoch 1/1... Batch 1630... Discriminator Loss: 1.8612... Generator Loss: 0.2034
Epoch 1/1... Batch 1640... Discriminator Loss: 1.0642... Generator Loss: 0.6049
Epoch 1/1... Batch 1650... Discriminator Loss: 0.7964... Generator Loss: 1.4391
Epoch 1/1... Batch 1660... Discriminator Loss: 2.9947... Generator Loss: 3.7338
Epoch 1/1... Batch 1670... Discriminator Loss: 0.2392... Generator Loss: 1.9595
Epoch 1/1... Batch 1680... Discriminator Loss: 1.2833... Generator Loss: 0.6244
Epoch 1/1... Batch 1690... Discriminator Loss: 1.1459... Generator Loss: 0.5714
Epoch 1/1... Batch 1700... Discriminator Loss: 1.5987... Generator Loss: 2.4427
Epoch 1/1... Batch 1710... Discriminator Loss: 1.1600... Generator Loss: 0.5224
Epoch 1/1... Batch 1720... Discriminator Loss: 0.6816... Generator Loss: 1.3494
Epoch 1/1... Batch 1730... Discriminator Loss: 0.9889... Generator Loss: 0.8001
Epoch 1/1... Batch 1740... Discriminator Loss: 0.7332... Generator Loss: 1.3606
Epoch 1/1... Batch 1750... Discriminator Loss: 1.1460... Generator Loss: 0.5330
Epoch 1/1... Batch 1760... Discriminator Loss: 0.6075... Generator Loss: 1.0020
Epoch 1/1... Batch 1770... Discriminator Loss: 1.0205... Generator Loss: 0.6259
Epoch 1/1... Batch 1780... Discriminator Loss: 1.5236... Generator Loss: 0.3592
Epoch 1/1... Batch 1790... Discriminator Loss: 1.1990... Generator Loss: 0.4782
Epoch 1/1... Batch 1800... Discriminator Loss: 1.0393... Generator Loss: 0.5580
Epoch 1/1... Batch 1810... Discriminator Loss: 0.9233... Generator Loss: 0.6972
Epoch 1/1... Batch 1820... Discriminator Loss: 1.2142... Generator Loss: 2.5967
Epoch 1/1... Batch 1830... Discriminator Loss: 0.8349... Generator Loss: 1.0711
Epoch 1/1... Batch 1840... Discriminator Loss: 1.4459... Generator Loss: 2.2395
Epoch 1/1... Batch 1850... Discriminator Loss: 1.2094... Generator Loss: 0.5343
Epoch 1/1... Batch 1860... Discriminator Loss: 0.4808... Generator Loss: 1.1662
Epoch 1/1... Batch 1870... Discriminator Loss: 3.5437... Generator Loss: 3.4733
Epoch 1/1... Batch 1880... Discriminator Loss: 1.4677... Generator Loss: 0.9333
Epoch 1/1... Batch 1890... Discriminator Loss: 1.0802... Generator Loss: 0.9168
Epoch 1/1... Batch 1900... Discriminator Loss: 0.8032... Generator Loss: 0.9026
Epoch 1/1... Batch 1910... Discriminator Loss: 1.1482... Generator Loss: 1.9072
Epoch 1/1... Batch 1920... Discriminator Loss: 2.2567... Generator Loss: 3.4766
Epoch 1/1... Batch 1930... Discriminator Loss: 0.7290... Generator Loss: 1.5556
Epoch 1/1... Batch 1940... Discriminator Loss: 0.3868... Generator Loss: 1.6457
Epoch 1/1... Batch 1950... Discriminator Loss: 1.2048... Generator Loss: 0.4622
Epoch 1/1... Batch 1960... Discriminator Loss: 3.2142... Generator Loss: 3.5746
Epoch 1/1... Batch 1970... Discriminator Loss: 1.0687... Generator Loss: 0.8045
Epoch 1/1... Batch 1980... Discriminator Loss: 0.7934... Generator Loss: 0.8361
Epoch 1/1... Batch 1990... Discriminator Loss: 0.9369... Generator Loss: 0.6262
Epoch 1/1... Batch 2000... Discriminator Loss: 1.0145... Generator Loss: 0.7350
Epoch 1/1... Batch 2010... Discriminator Loss: 0.8933... Generator Loss: 0.9987
Epoch 1/1... Batch 2020... Discriminator Loss: 1.1495... Generator Loss: 0.4811
Epoch 1/1... Batch 2030... Discriminator Loss: 0.5322... Generator Loss: 1.1052
Epoch 1/1... Batch 2040... Discriminator Loss: 0.6708... Generator Loss: 2.1118
Epoch 1/1... Batch 2050... Discriminator Loss: 1.2269... Generator Loss: 0.5495
Epoch 1/1... Batch 2060... Discriminator Loss: 0.5578... Generator Loss: 1.2945
Epoch 1/1... Batch 2070... Discriminator Loss: 1.5058... Generator Loss: 0.3360
Epoch 1/1... Batch 2080... Discriminator Loss: 0.6530... Generator Loss: 1.0590
Epoch 1/1... Batch 2090... Discriminator Loss: 1.2150... Generator Loss: 0.5077
Epoch 1/1... Batch 2100... Discriminator Loss: 1.0830... Generator Loss: 2.5640
Epoch 1/1... Batch 2110... Discriminator Loss: 1.5232... Generator Loss: 0.2987
Epoch 1/1... Batch 2120... Discriminator Loss: 0.5757... Generator Loss: 1.1137
Epoch 1/1... Batch 2130... Discriminator Loss: 1.2899... Generator Loss: 0.5262
Epoch 1/1... Batch 2140... Discriminator Loss: 0.4150... Generator Loss: 1.6724
Epoch 1/1... Batch 2150... Discriminator Loss: 1.0415... Generator Loss: 1.4239
Epoch 1/1... Batch 2160... Discriminator Loss: 1.0382... Generator Loss: 0.6683
Epoch 1/1... Batch 2170... Discriminator Loss: 0.4941... Generator Loss: 1.7538
Epoch 1/1... Batch 2180... Discriminator Loss: 0.8601... Generator Loss: 0.7038
Epoch 1/1... Batch 2190... Discriminator Loss: 1.0544... Generator Loss: 1.0376
Epoch 1/1... Batch 2200... Discriminator Loss: 1.3197... Generator Loss: 0.4475
Epoch 1/1... Batch 2210... Discriminator Loss: 1.2936... Generator Loss: 0.5035
Epoch 1/1... Batch 2220... Discriminator Loss: 0.9698... Generator Loss: 0.7154
Epoch 1/1... Batch 2230... Discriminator Loss: 1.1087... Generator Loss: 0.6053
Epoch 1/1... Batch 2240... Discriminator Loss: 1.4593... Generator Loss: 0.5648
Epoch 1/1... Batch 2250... Discriminator Loss: 1.0520... Generator Loss: 0.5365
Epoch 1/1... Batch 2260... Discriminator Loss: 1.3641... Generator Loss: 0.3893
Epoch 1/1... Batch 2270... Discriminator Loss: 0.5876... Generator Loss: 1.9636
Epoch 1/1... Batch 2280... Discriminator Loss: 0.9447... Generator Loss: 0.7345
Epoch 1/1... Batch 2290... Discriminator Loss: 1.6494... Generator Loss: 2.3369
Epoch 1/1... Batch 2300... Discriminator Loss: 1.4828... Generator Loss: 0.3884
Epoch 1/1... Batch 2310... Discriminator Loss: 1.0586... Generator Loss: 0.8973
Epoch 1/1... Batch 2320... Discriminator Loss: 0.5018... Generator Loss: 1.7938
Epoch 1/1... Batch 2330... Discriminator Loss: 1.0633... Generator Loss: 0.6903
Epoch 1/1... Batch 2340... Discriminator Loss: 1.0563... Generator Loss: 0.6193
Epoch 1/1... Batch 2350... Discriminator Loss: 1.2048... Generator Loss: 0.6543
Epoch 1/1... Batch 2360... Discriminator Loss: 1.2792... Generator Loss: 0.4988
Epoch 1/1... Batch 2370... Discriminator Loss: 0.8906... Generator Loss: 1.5482
Epoch 1/1... Batch 2380... Discriminator Loss: 1.5690... Generator Loss: 0.3191
Epoch 1/1... Batch 2390... Discriminator Loss: 1.0544... Generator Loss: 0.5842
Epoch 1/1... Batch 2400... Discriminator Loss: 1.4158... Generator Loss: 1.0860
Epoch 1/1... Batch 2410... Discriminator Loss: 1.8587... Generator Loss: 2.8478
Epoch 1/1... Batch 2420... Discriminator Loss: 1.2020... Generator Loss: 0.5980
Epoch 1/1... Batch 2430... Discriminator Loss: 1.0522... Generator Loss: 0.5586
Epoch 1/1... Batch 2440... Discriminator Loss: 0.7915... Generator Loss: 0.9804
Epoch 1/1... Batch 2450... Discriminator Loss: 0.7220... Generator Loss: 1.0344
Epoch 1/1... Batch 2460... Discriminator Loss: 0.8025... Generator Loss: 1.0285
Epoch 1/1... Batch 2470... Discriminator Loss: 1.3331... Generator Loss: 0.4332
Epoch 1/1... Batch 2480... Discriminator Loss: 1.2555... Generator Loss: 0.4931
Epoch 1/1... Batch 2490... Discriminator Loss: 1.2439... Generator Loss: 0.4884
Epoch 1/1... Batch 2500... Discriminator Loss: 0.7209... Generator Loss: 1.2423
Epoch 1/1... Batch 2510... Discriminator Loss: 0.8907... Generator Loss: 3.0200
Epoch 1/1... Batch 2520... Discriminator Loss: 1.5855... Generator Loss: 2.1168
Epoch 1/1... Batch 2530... Discriminator Loss: 0.9139... Generator Loss: 0.6935
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Epoch 1/1... Batch 5350... Discriminator Loss: 1.4817... Generator Loss: 0.3617
Epoch 1/1... Batch 5360... Discriminator Loss: 1.6006... Generator Loss: 0.3365
Epoch 1/1... Batch 5370... Discriminator Loss: 0.9863... Generator Loss: 0.6768
Epoch 1/1... Batch 5380... Discriminator Loss: 1.0556... Generator Loss: 0.6744
Epoch 1/1... Batch 5390... Discriminator Loss: 1.0538... Generator Loss: 0.9024
Epoch 1/1... Batch 5400... Discriminator Loss: 0.7530... Generator Loss: 0.9476
Epoch 1/1... Batch 5410... Discriminator Loss: 1.4141... Generator Loss: 0.3915
Epoch 1/1... Batch 5420... Discriminator Loss: 0.9077... Generator Loss: 1.5040
Epoch 1/1... Batch 5430... Discriminator Loss: 1.2395... Generator Loss: 0.4999
Epoch 1/1... Batch 5440... Discriminator Loss: 1.0438... Generator Loss: 0.8689
Epoch 1/1... Batch 5450... Discriminator Loss: 0.9228... Generator Loss: 0.8433
Epoch 1/1... Batch 5460... Discriminator Loss: 1.2637... Generator Loss: 0.4503
Epoch 1/1... Batch 5470... Discriminator Loss: 1.3528... Generator Loss: 0.4263
Epoch 1/1... Batch 5480... Discriminator Loss: 1.2101... Generator Loss: 1.8875
Epoch 1/1... Batch 5490... Discriminator Loss: 1.7387... Generator Loss: 0.2315
Epoch 1/1... Batch 5500... Discriminator Loss: 1.0167... Generator Loss: 0.7814
Epoch 1/1... Batch 5510... Discriminator Loss: 1.4496... Generator Loss: 0.3335
Epoch 1/1... Batch 5520... Discriminator Loss: 1.0645... Generator Loss: 0.6452
Epoch 1/1... Batch 5530... Discriminator Loss: 1.4804... Generator Loss: 0.3158
Epoch 1/1... Batch 5540... Discriminator Loss: 1.6019... Generator Loss: 0.3161
Epoch 1/1... Batch 5550... Discriminator Loss: 1.3979... Generator Loss: 0.8995
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Epoch 1/1... Batch 5570... Discriminator Loss: 1.1404... Generator Loss: 1.3977
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Epoch 1/1... Batch 5590... Discriminator Loss: 0.9032... Generator Loss: 0.8506
Epoch 1/1... Batch 5600... Discriminator Loss: 1.4468... Generator Loss: 0.4420
Epoch 1/1... Batch 5610... Discriminator Loss: 1.2211... Generator Loss: 0.5065
Epoch 1/1... Batch 5620... Discriminator Loss: 1.0691... Generator Loss: 0.6961
Epoch 1/1... Batch 5630... Discriminator Loss: 1.7113... Generator Loss: 0.2483
Epoch 1/1... Batch 5640... Discriminator Loss: 0.8610... Generator Loss: 1.6002
Epoch 1/1... Batch 5650... Discriminator Loss: 1.3517... Generator Loss: 0.4907
Epoch 1/1... Batch 5660... Discriminator Loss: 1.5199... Generator Loss: 0.3608
Epoch 1/1... Batch 5670... Discriminator Loss: 1.6189... Generator Loss: 0.3039
Epoch 1/1... Batch 5680... Discriminator Loss: 1.0900... Generator Loss: 0.6515
Epoch 1/1... Batch 5690... Discriminator Loss: 1.1454... Generator Loss: 0.6992
Epoch 1/1... Batch 5700... Discriminator Loss: 1.3283... Generator Loss: 0.4409
Epoch 1/1... Batch 5710... Discriminator Loss: 0.9162... Generator Loss: 0.7508
Epoch 1/1... Batch 5720... Discriminator Loss: 0.5900... Generator Loss: 1.2723
Epoch 1/1... Batch 5730... Discriminator Loss: 1.1688... Generator Loss: 0.5283
Epoch 1/1... Batch 5740... Discriminator Loss: 1.0398... Generator Loss: 0.9162
Epoch 1/1... Batch 5750... Discriminator Loss: 1.1046... Generator Loss: 0.6543
Epoch 1/1... Batch 5760... Discriminator Loss: 1.0618... Generator Loss: 1.4622
Epoch 1/1... Batch 5770... Discriminator Loss: 1.2824... Generator Loss: 0.6005
Epoch 1/1... Batch 5780... Discriminator Loss: 1.1313... Generator Loss: 0.7734
Epoch 1/1... Batch 5790... Discriminator Loss: 1.4342... Generator Loss: 0.3825
Epoch 1/1... Batch 5800... Discriminator Loss: 1.5503... Generator Loss: 0.3193
Epoch 1/1... Batch 5810... Discriminator Loss: 1.6003... Generator Loss: 0.3024
Epoch 1/1... Batch 5820... Discriminator Loss: 0.8807... Generator Loss: 0.7692
Epoch 1/1... Batch 5830... Discriminator Loss: 1.1794... Generator Loss: 1.5318
Epoch 1/1... Batch 5840... Discriminator Loss: 1.3346... Generator Loss: 0.5149
Epoch 1/1... Batch 5850... Discriminator Loss: 1.3147... Generator Loss: 2.5212
Epoch 1/1... Batch 5860... Discriminator Loss: 1.0373... Generator Loss: 1.7908
Epoch 1/1... Batch 5870... Discriminator Loss: 1.1928... Generator Loss: 1.3622
Epoch 1/1... Batch 5880... Discriminator Loss: 1.1190... Generator Loss: 0.5943
Epoch 1/1... Batch 5890... Discriminator Loss: 1.0929... Generator Loss: 1.1551
Epoch 1/1... Batch 5900... Discriminator Loss: 1.1529... Generator Loss: 0.6196
Epoch 1/1... Batch 5910... Discriminator Loss: 1.1654... Generator Loss: 0.5746
Epoch 1/1... Batch 5920... Discriminator Loss: 1.0406... Generator Loss: 1.0201
Epoch 1/1... Batch 5930... Discriminator Loss: 1.4314... Generator Loss: 0.3699
Epoch 1/1... Batch 5940... Discriminator Loss: 1.6853... Generator Loss: 0.2492
Epoch 1/1... Batch 5950... Discriminator Loss: 1.0834... Generator Loss: 0.6670
Epoch 1/1... Batch 5960... Discriminator Loss: 1.0452... Generator Loss: 0.8167
Epoch 1/1... Batch 5970... Discriminator Loss: 1.8909... Generator Loss: 0.2235
Epoch 1/1... Batch 5980... Discriminator Loss: 1.2917... Generator Loss: 0.4220
Epoch 1/1... Batch 5990... Discriminator Loss: 0.9719... Generator Loss: 0.7467
Epoch 1/1... Batch 6000... Discriminator Loss: 0.6638... Generator Loss: 0.9620
Epoch 1/1... Batch 6010... Discriminator Loss: 1.8564... Generator Loss: 0.2241
Epoch 1/1... Batch 6020... Discriminator Loss: 1.4622... Generator Loss: 0.3515
Epoch 1/1... Batch 6030... Discriminator Loss: 1.9438... Generator Loss: 0.1958
Epoch 1/1... Batch 6040... Discriminator Loss: 1.3034... Generator Loss: 0.4717
Epoch 1/1... Batch 6050... Discriminator Loss: 1.0968... Generator Loss: 0.5091
Epoch 1/1... Batch 6060... Discriminator Loss: 1.3775... Generator Loss: 0.4206
Epoch 1/1... Batch 6070... Discriminator Loss: 1.1471... Generator Loss: 0.6813
Epoch 1/1... Batch 6080... Discriminator Loss: 1.1789... Generator Loss: 0.5786
Epoch 1/1... Batch 6090... Discriminator Loss: 1.1443... Generator Loss: 0.5620
Epoch 1/1... Batch 6100... Discriminator Loss: 0.9241... Generator Loss: 1.0852
Epoch 1/1... Batch 6110... Discriminator Loss: 1.4901... Generator Loss: 0.3441
Epoch 1/1... Batch 6120... Discriminator Loss: 1.5115... Generator Loss: 0.3903
Epoch 1/1... Batch 6130... Discriminator Loss: 0.9028... Generator Loss: 1.3410
Epoch 1/1... Batch 6140... Discriminator Loss: 1.4642... Generator Loss: 1.3551
Epoch 1/1... Batch 6150... Discriminator Loss: 0.9940... Generator Loss: 0.7207
Epoch 1/1... Batch 6160... Discriminator Loss: 0.8937... Generator Loss: 0.7490
Epoch 1/1... Batch 6170... Discriminator Loss: 1.0271... Generator Loss: 0.6762
Epoch 1/1... Batch 6180... Discriminator Loss: 0.7839... Generator Loss: 1.1329
Epoch 1/1... Batch 6190... Discriminator Loss: 1.0696... Generator Loss: 0.5897
Epoch 1/1... Batch 6200... Discriminator Loss: 1.1498... Generator Loss: 0.6054
Epoch 1/1... Batch 6210... Discriminator Loss: 2.0931... Generator Loss: 0.1872
Epoch 1/1... Batch 6220... Discriminator Loss: 1.2624... Generator Loss: 0.7682
Epoch 1/1... Batch 6230... Discriminator Loss: 1.8159... Generator Loss: 0.2123
Epoch 1/1... Batch 6240... Discriminator Loss: 1.3004... Generator Loss: 0.5090
Epoch 1/1... Batch 6250... Discriminator Loss: 0.7798... Generator Loss: 1.0725
Epoch 1/1... Batch 6260... Discriminator Loss: 1.1478... Generator Loss: 0.5534
Epoch 1/1... Batch 6270... Discriminator Loss: 1.1013... Generator Loss: 0.9388
Epoch 1/1... Batch 6280... Discriminator Loss: 1.0065... Generator Loss: 0.9927
Epoch 1/1... Batch 6290... Discriminator Loss: 1.3105... Generator Loss: 0.4438
Epoch 1/1... Batch 6300... Discriminator Loss: 1.1994... Generator Loss: 0.5676
Epoch 1/1... Batch 6310... Discriminator Loss: 1.1673... Generator Loss: 0.9967
Epoch 1/1... Batch 6320... Discriminator Loss: 1.0439... Generator Loss: 0.9328
Epoch 1/1... Batch 6330... Discriminator Loss: 1.0246... Generator Loss: 0.9961

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.